@inproceedings{boros-chivereanu-2026-racai,
    title = "{RACAI} at {\#}{SMM}4{H}-{H}ea{RD}: Named Entity Recognition for Detecting the Impacts of Drug Abuse in Social Media Posts: Zero-Shot and Fine-Tuning Approaches",
    author = "Boros, Tiberiu  and
      Chivereanu, Radu-Gabriel",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.20/",
    pages = "121--126",
    ISBN = "979-8-89176-432-3",
    abstract = "In this work, we address the detection of drug abuse repercussions in Reddit posts, as part of SMM4H-HeaRD Task 7: Extraction of Social and Clinical Impacts of Substance Use from Social Media Posts. We evaluate multiple approaches, including fine-tuning and zero-shot inference, across several deep learning architectures. Our best result is obtained using an adapter-based fine-tuning approach on the DeBERTaV3 model. In addition, we explore text-based evolutionary optimization for Gemma 4 workflows and show that, on this task, they achieve competitive performance with the supervised DeBERTaV3 setup."
}